Overview

Dataset statistics

Number of variables41
Number of observations225493
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory63.0 MiB
Average record size in memory293.0 B

Variable types

Numeric29
DateTime2
Categorical5
Boolean5

Alerts

AVERAGE.ACCT.AGE has a high cardinality: 192 distinct values High cardinality
CREDIT.HISTORY.LENGTH has a high cardinality: 291 distinct values High cardinality
df_index is highly correlated with Employee_code_IDHigh correlation
disbursed_amount is highly correlated with asset_costHigh correlation
asset_cost is highly correlated with disbursed_amountHigh correlation
State_ID is highly correlated with branch_id and 3 other fieldsHigh correlation
Employee_code_ID is highly correlated with df_indexHigh correlation
PERFORM_CNS.SCORE is highly correlated with PERFORM_CNS.SCORE.DESCRIPTIONHigh correlation
PRI.NO.OF.ACCTS is highly correlated with PRI.ACTIVE.ACCTS and 1 other fieldsHigh correlation
PRI.ACTIVE.ACCTS is highly correlated with PERFORM_CNS.SCORE.DESCRIPTION and 5 other fieldsHigh correlation
PRI.CURRENT.BALANCE is highly correlated with PRI.ACTIVE.ACCTS and 3 other fieldsHigh correlation
PRI.SANCTIONED.AMOUNT is highly correlated with PRI.ACTIVE.ACCTS and 3 other fieldsHigh correlation
PRI.DISBURSED.AMOUNT is highly correlated with PRI.ACTIVE.ACCTS and 3 other fieldsHigh correlation
SEC.NO.OF.ACCTS is highly correlated with SEC.ACTIVE.ACCTS and 1 other fieldsHigh correlation
SEC.ACTIVE.ACCTS is highly correlated with SEC.NO.OF.ACCTS and 4 other fieldsHigh correlation
SEC.CURRENT.BALANCE is highly correlated with SEC.ACTIVE.ACCTS and 2 other fieldsHigh correlation
SEC.SANCTIONED.AMOUNT is highly correlated with SEC.ACTIVE.ACCTS and 2 other fieldsHigh correlation
SEC.DISBURSED.AMOUNT is highly correlated with SEC.ACTIVE.ACCTS and 2 other fieldsHigh correlation
PRIMARY.INSTAL.AMT is highly correlated with PRI.NO.OF.ACCTS and 4 other fieldsHigh correlation
SEC.INSTAL.AMT is highly correlated with SEC.NO.OF.ACCTS and 4 other fieldsHigh correlation
NEW.ACCTS.IN.LAST.SIX.MONTHS is highly correlated with PRI.NO.OF.ACCTS and 4 other fieldsHigh correlation
Aadhar_flag is highly correlated with State_ID and 1 other fieldsHigh correlation
VoterID_flag is highly correlated with State_ID and 1 other fieldsHigh correlation
PERFORM_CNS.SCORE.DESCRIPTION is highly correlated with PERFORM_CNS.SCORE and 1 other fieldsHigh correlation
SEC.OVERDUE.ACCTS is highly correlated with SEC.NO.OF.ACCTS and 1 other fieldsHigh correlation
UniqueID is highly correlated with DisbursalDateHigh correlation
branch_id is highly correlated with Current_pincode_ID and 1 other fieldsHigh correlation
Current_pincode_ID is highly correlated with branch_id and 1 other fieldsHigh correlation
DisbursalDate is highly correlated with UniqueIDHigh correlation
PRI.CURRENT.BALANCE is highly skewed (γ1 = 29.25624693) Skewed
PRI.SANCTIONED.AMOUNT is highly skewed (γ1 = 319.533663) Skewed
PRI.DISBURSED.AMOUNT is highly skewed (γ1 = 318.4004683) Skewed
SEC.NO.OF.ACCTS is highly skewed (γ1 = 27.84235193) Skewed
SEC.ACTIVE.ACCTS is highly skewed (γ1 = 30.4096604) Skewed
SEC.OVERDUE.ACCTS is highly skewed (γ1 = 24.01431522) Skewed
SEC.CURRENT.BALANCE is highly skewed (γ1 = 107.0091863) Skewed
SEC.SANCTIONED.AMOUNT is highly skewed (γ1 = 74.21689332) Skewed
SEC.DISBURSED.AMOUNT is highly skewed (γ1 = 74.71985798) Skewed
PRIMARY.INSTAL.AMT is highly skewed (γ1 = 71.5253121) Skewed
SEC.INSTAL.AMT is highly skewed (γ1 = 152.8457066) Skewed
df_index is uniformly distributed Uniform
df_index has unique values Unique
UniqueID has unique values Unique
PERFORM_CNS.SCORE has 111773 (49.6%) zeros Zeros
PRI.NO.OF.ACCTS has 111773 (49.6%) zeros Zeros
PRI.ACTIVE.ACCTS has 131395 (58.3%) zeros Zeros
PRI.OVERDUE.ACCTS has 199703 (88.6%) zeros Zeros
PRI.CURRENT.BALANCE has 136011 (60.3%) zeros Zeros
PRI.SANCTIONED.AMOUNT has 132449 (58.7%) zeros Zeros
PRI.DISBURSED.AMOUNT has 132559 (58.8%) zeros Zeros
SEC.NO.OF.ACCTS has 219731 (97.4%) zeros Zeros
SEC.ACTIVE.ACCTS has 221737 (98.3%) zeros Zeros
SEC.OVERDUE.ACCTS has 224183 (99.4%) zeros Zeros
SEC.CURRENT.BALANCE has 222182 (98.5%) zeros Zeros
SEC.SANCTIONED.AMOUNT has 221816 (98.4%) zeros Zeros
SEC.DISBURSED.AMOUNT has 221846 (98.4%) zeros Zeros
PRIMARY.INSTAL.AMT has 153544 (68.1%) zeros Zeros
SEC.INSTAL.AMT has 223313 (99.0%) zeros Zeros
NEW.ACCTS.IN.LAST.SIX.MONTHS has 174944 (77.6%) zeros Zeros
DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS has 207647 (92.1%) zeros Zeros
NO.OF_INQUIRIES has 194990 (86.5%) zeros Zeros

Reproduction

Analysis started2022-11-01 12:15:50.320040
Analysis finished2022-11-01 12:24:31.730569
Duration8 minutes and 41.41 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct225493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116840.0736
Minimum0
Maximum233153
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:32.005708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11860.6
Q158759
median116929
Q3175107
95-th percentile221622.4
Maximum233153
Range233153
Interquartile range (IQR)116348

Descriptive statistics

Standard deviation67261.70244
Coefficient of variation (CV)0.5756732289
Kurtosis-1.196648141
Mean116840.0736
Median Absolute Deviation (MAD)58174
Skewness-0.004018369466
Sum2.634661872 × 1010
Variance4524136616
MonotonicityStrictly increasing
2022-11-01T17:54:32.429580image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
1556171
 
< 0.1%
1556061
 
< 0.1%
1556071
 
< 0.1%
1556081
 
< 0.1%
1556091
 
< 0.1%
1556101
 
< 0.1%
1556111
 
< 0.1%
1556121
 
< 0.1%
1556131
 
< 0.1%
Other values (225483)225483
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
2331531
< 0.1%
2331521
< 0.1%
2331511
< 0.1%
2331501
< 0.1%
2331491
< 0.1%
2331481
< 0.1%
2331471
< 0.1%
2331461
< 0.1%
2331451
< 0.1%
2331441
< 0.1%

UniqueID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct225493
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean535677.4538
Minimum417428
Maximum671084
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:32.745611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum417428
5-th percentile429179.6
Q1476481
median535593
Q3594774
95-th percentile642328.4
Maximum671084
Range253656
Interquartile range (IQR)118293

Descriptive statistics

Standard deviation68337.22275
Coefficient of variation (CV)0.1275715867
Kurtosis-1.198808756
Mean535677.4538
Median Absolute Deviation (MAD)59148
Skewness0.001876839031
Sum1.207915161 × 1011
Variance4669976013
MonotonicityNot monotonic
2022-11-01T17:54:33.109584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4208251
 
< 0.1%
4925341
 
< 0.1%
5854171
 
< 0.1%
5719391
 
< 0.1%
5918401
 
< 0.1%
6316301
 
< 0.1%
4212331
 
< 0.1%
5858751
 
< 0.1%
4903461
 
< 0.1%
6443681
 
< 0.1%
Other values (225483)225483
> 99.9%
ValueCountFrequency (%)
4174281
< 0.1%
4174291
< 0.1%
4174301
< 0.1%
4174311
< 0.1%
4174321
< 0.1%
4174331
< 0.1%
4174341
< 0.1%
4174351
< 0.1%
4174361
< 0.1%
4174371
< 0.1%
ValueCountFrequency (%)
6710841
< 0.1%
6710331
< 0.1%
6586761
< 0.1%
6586751
< 0.1%
6586741
< 0.1%
6586731
< 0.1%
6586721
< 0.1%
6586711
< 0.1%
6586701
< 0.1%
6586691
< 0.1%

disbursed_amount
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24228
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54240.72883
Minimum13320
Maximum987354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:33.374506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13320
5-th percentile34839
Q147049
median53703
Q360213
95-th percentile73817
Maximum987354
Range974034
Interquartile range (IQR)13164

Descriptive statistics

Standard deviation12775.59006
Coefficient of variation (CV)0.2355349999
Kurtosis146.7750226
Mean54240.72883
Median Absolute Deviation (MAD)6558
Skewness3.069299613
Sum1.223090467 × 1010
Variance163215701.4
MonotonicityNot monotonic
2022-11-01T17:54:33.628549image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
483492076
 
0.9%
533032034
 
0.9%
503031915
 
0.8%
513031913
 
0.8%
523031814
 
0.8%
473491814
 
0.8%
552591795
 
0.8%
463491569
 
0.7%
562591534
 
0.7%
572591521
 
0.7%
Other values (24218)207508
92.0%
ValueCountFrequency (%)
133201
 
< 0.1%
133691
 
< 0.1%
136001
 
< 0.1%
136401
 
< 0.1%
136521
 
< 0.1%
136646
< 0.1%
138141
 
< 0.1%
139141
 
< 0.1%
139401
 
< 0.1%
139411
 
< 0.1%
ValueCountFrequency (%)
9873541
< 0.1%
5924601
< 0.1%
3320451
< 0.1%
3185331
< 0.1%
3159041
< 0.1%
2377791
< 0.1%
1969981
< 0.1%
1913921
< 0.1%
1908871
< 0.1%
1877871
< 0.1%

asset_cost
Real number (ℝ≥0)

HIGH CORRELATION

Distinct45415
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75631.13188
Minimum37000
Maximum1328954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:33.902276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum37000
5-th percentile58170
Q165625
median70807
Q378966
95-th percentile109031.8
Maximum1328954
Range1291954
Interquartile range (IQR)13341

Descriptive statistics

Standard deviation18527.57573
Coefficient of variation (CV)0.2449728738
Kurtosis110.3653432
Mean75631.13188
Median Absolute Deviation (MAD)6157
Skewness4.055663725
Sum1.705429082 × 1010
Variance343271062.2
MonotonicityNot monotonic
2022-11-01T17:54:34.156523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68000674
 
0.3%
67000583
 
0.3%
72000526
 
0.2%
70000491
 
0.2%
74000461
 
0.2%
66000457
 
0.2%
75000453
 
0.2%
73000450
 
0.2%
69000430
 
0.2%
65000392
 
0.2%
Other values (45405)220576
97.8%
ValueCountFrequency (%)
370002
< 0.1%
371291
< 0.1%
372301
< 0.1%
373101
< 0.1%
373771
< 0.1%
376581
< 0.1%
378161
< 0.1%
380552
< 0.1%
380591
< 0.1%
380631
< 0.1%
ValueCountFrequency (%)
13289541
< 0.1%
7151861
< 0.1%
4596251
< 0.1%
3880251
< 0.1%
3836001
< 0.1%
3780922
< 0.1%
2863501
< 0.1%
2811641
< 0.1%
2801001
< 0.1%
2776001
< 0.1%

ltv
Real number (ℝ≥0)

Distinct6541
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.80663386
Minimum13.5
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:34.414536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum13.5
5-th percentile52.42
Q168.96
median76.89
Q383.73
95-th percentile89.39
Maximum95
Range81.5
Interquartile range (IQR)14.77

Descriptive statistics

Standard deviation11.4418905
Coefficient of variation (CV)0.1529528854
Kurtosis1.293300679
Mean74.80663386
Median Absolute Deviation (MAD)7.25
Skewness-1.076667482
Sum16868372.29
Variance130.9168582
MonotonicityNot monotonic
2022-11-01T17:54:34.636688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
854298
 
1.9%
84.991018
 
0.5%
79.99536
 
0.2%
80480
 
0.2%
75415
 
0.2%
79.9402
 
0.2%
79.79387
 
0.2%
74.93374
 
0.2%
90328
 
0.1%
89.86327
 
0.1%
Other values (6531)216928
96.2%
ValueCountFrequency (%)
13.51
< 0.1%
14.171
< 0.1%
15.31
< 0.1%
15.581
< 0.1%
16.61
< 0.1%
17.021
< 0.1%
17.051
< 0.1%
17.131
< 0.1%
17.361
< 0.1%
181
< 0.1%
ValueCountFrequency (%)
958
 
< 0.1%
94.997
 
< 0.1%
94.989
< 0.1%
94.975
 
< 0.1%
94.9611
< 0.1%
94.9514
< 0.1%
94.9413
< 0.1%
94.9320
< 0.1%
94.9217
< 0.1%
94.9113
< 0.1%

branch_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct82
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.07061417
Minimum1
Maximum261
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:34.880622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q114
median61
Q3130
95-th percentile249
Maximum261
Range260
Interquartile range (IQR)116

Descriptive statistics

Standard deviation70.01414708
Coefficient of variation (CV)0.9581710497
Kurtosis0.302701006
Mean73.07061417
Median Absolute Deviation (MAD)50
Skewness1.032784512
Sum16476912
Variance4901.980791
MonotonicityNot monotonic
2022-11-01T17:54:35.120268image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
213003
 
5.8%
6710858
 
4.8%
39214
 
4.1%
59096
 
4.0%
368818
 
3.9%
347794
 
3.5%
1367128
 
3.2%
195843
 
2.6%
165592
 
2.5%
15306
 
2.4%
Other values (72)142841
63.3%
ValueCountFrequency (%)
15306
2.4%
213003
5.8%
39214
4.1%
59096
4.0%
72985
 
1.3%
82965
 
1.3%
92354
 
1.0%
103848
 
1.7%
114078
 
1.8%
132889
 
1.3%
ValueCountFrequency (%)
261176
 
0.1%
260339
 
0.2%
259345
 
0.2%
258374
 
0.2%
2571256
 
0.6%
2551562
0.7%
2541699
0.8%
2513842
1.7%
2501442
 
0.6%
249854
 
0.4%

supplier_id
Real number (ℝ≥0)

Distinct2945
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19645.59789
Minimum10524
Maximum24803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:35.363733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10524
5-th percentile14181
Q116555
median20333
Q323004
95-th percentile24124
Maximum24803
Range14279
Interquartile range (IQR)6449

Descriptive statistics

Standard deviation3494.023799
Coefficient of variation (CV)0.1778527596
Kurtosis-1.478569771
Mean19645.59789
Median Absolute Deviation (MAD)3061
Skewness-0.1710645231
Sum4429944805
Variance12208202.31
MonotonicityNot monotonic
2022-11-01T17:54:35.599662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183171319
 
0.6%
179801252
 
0.6%
142341241
 
0.6%
156631237
 
0.5%
156941154
 
0.5%
181661145
 
0.5%
143751103
 
0.5%
141151042
 
0.5%
141451037
 
0.5%
227271012
 
0.4%
Other values (2935)213951
94.9%
ValueCountFrequency (%)
105246
 
< 0.1%
123113
 
< 0.1%
1231238
< 0.1%
1237486
< 0.1%
1244147
< 0.1%
1245668
< 0.1%
1250054
< 0.1%
1253455
< 0.1%
125397
 
< 0.1%
1279761
< 0.1%
ValueCountFrequency (%)
248032
< 0.1%
248022
< 0.1%
247991
 
< 0.1%
247972
< 0.1%
247941
 
< 0.1%
247931
 
< 0.1%
247901
 
< 0.1%
247891
 
< 0.1%
247872
< 0.1%
247853
< 0.1%

manufacturer_id
Real number (ℝ≥0)

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.07225058
Minimum45
Maximum156
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:35.808873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile45
Q148
median86
Q386
95-th percentile86
Maximum156
Range111
Interquartile range (IQR)38

Descriptive statistics

Standard deviation22.16467967
Coefficient of variation (CV)0.3208912332
Kurtosis-0.7184451107
Mean69.07225058
Median Absolute Deviation (MAD)34
Skewness0.3871002259
Sum15575309
Variance491.2730247
MonotonicityNot monotonic
2022-11-01T17:54:35.968822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
86106062
47.0%
4555207
24.5%
5126243
 
11.6%
4815721
 
7.0%
499700
 
4.3%
1209417
 
4.2%
672366
 
1.0%
145760
 
0.3%
15311
 
< 0.1%
1525
 
< 0.1%
ValueCountFrequency (%)
4555207
24.5%
4815721
 
7.0%
499700
 
4.3%
5126243
 
11.6%
672366
 
1.0%
86106062
47.0%
1209417
 
4.2%
145760
 
0.3%
1525
 
< 0.1%
15311
 
< 0.1%
ValueCountFrequency (%)
1561
 
< 0.1%
15311
 
< 0.1%
1525
 
< 0.1%
145760
 
0.3%
1209417
 
4.2%
86106062
47.0%
672366
 
1.0%
5126243
 
11.6%
499700
 
4.3%
4815721
 
7.0%

Current_pincode_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6659
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3375.718133
Minimum1
Maximum7345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:36.186660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile234
Q11509
median2949
Q35682
95-th percentile6944
Maximum7345
Range7344
Interquartile range (IQR)4173

Descriptive statistics

Standard deviation2253.216519
Coefficient of variation (CV)0.6674776834
Kurtosis-1.292294566
Mean3375.718133
Median Absolute Deviation (MAD)1902
Skewness0.2942958703
Sum761200809
Variance5076984.684
MonotonicityNot monotonic
2022-11-01T17:54:36.426587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25781852
 
0.8%
14461651
 
0.7%
15151044
 
0.5%
2989838
 
0.4%
2943834
 
0.4%
1509821
 
0.4%
2782818
 
0.4%
1794794
 
0.4%
571781
 
0.3%
3363727
 
0.3%
Other values (6649)215333
95.5%
ValueCountFrequency (%)
126
 
< 0.1%
272
 
< 0.1%
350
 
< 0.1%
487
< 0.1%
5215
0.1%
6100
< 0.1%
7104
< 0.1%
843
 
< 0.1%
929
 
< 0.1%
105
 
< 0.1%
ValueCountFrequency (%)
73457
 
< 0.1%
73441
 
< 0.1%
73432
 
< 0.1%
73421
 
< 0.1%
73417
 
< 0.1%
73402
 
< 0.1%
73382
 
< 0.1%
73373
 
< 0.1%
733620
< 0.1%
73351
 
< 0.1%
Distinct14417
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Minimum1972-01-01 00:00:00
Maximum2071-12-31 00:00:00
2022-11-01T17:54:36.715794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:54:36.958060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Employment.Type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Self employed
127635 
Salaried
97858 

Length

Max length13
Median length13
Mean length10.8301322
Min length8

Characters and Unicode

Total characters2442119
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalaried
2nd rowSelf employed
3rd rowSelf employed
4th rowSelf employed
5th rowSelf employed

Common Values

ValueCountFrequency (%)
Self employed127635
56.6%
Salaried97858
43.4%

Length

2022-11-01T17:54:37.189974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:54:37.384186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
self127635
36.1%
employed127635
36.1%
salaried97858
27.7%

Most occurring characters

ValueCountFrequency (%)
e480763
19.7%
l353128
14.5%
S225493
9.2%
d225493
9.2%
a195716
8.0%
f127635
 
5.2%
127635
 
5.2%
m127635
 
5.2%
p127635
 
5.2%
o127635
 
5.2%
Other values (3)323351
13.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2088991
85.5%
Uppercase Letter225493
 
9.2%
Space Separator127635
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e480763
23.0%
l353128
16.9%
d225493
10.8%
a195716
9.4%
f127635
 
6.1%
m127635
 
6.1%
p127635
 
6.1%
o127635
 
6.1%
y127635
 
6.1%
r97858
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
S225493
100.0%
Space Separator
ValueCountFrequency (%)
127635
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2314484
94.8%
Common127635
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e480763
20.8%
l353128
15.3%
S225493
9.7%
d225493
9.7%
a195716
8.5%
f127635
 
5.5%
m127635
 
5.5%
p127635
 
5.5%
o127635
 
5.5%
y127635
 
5.5%
Other values (2)195716
8.5%
Common
ValueCountFrequency (%)
127635
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2442119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e480763
19.7%
l353128
14.5%
S225493
9.2%
d225493
9.2%
a195716
8.0%
f127635
 
5.2%
127635
 
5.2%
m127635
 
5.2%
p127635
 
5.2%
o127635
 
5.2%
Other values (3)323351
13.2%

DisbursalDate
Date

HIGH CORRELATION

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
Minimum2018-01-08 00:00:00
Maximum2018-12-10 00:00:00
2022-11-01T17:54:37.563031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:54:37.798960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

State_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.241550735
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:38.021319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q310
95-th percentile16
Maximum22
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.460856315
Coefficient of variation (CV)0.6160084321
Kurtosis-0.2978858737
Mean7.241550735
Median Absolute Deviation (MAD)3
Skewness0.8287275014
Sum1632919
Variance19.89923906
MonotonicityNot monotonic
2022-11-01T17:54:38.193278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
444234
19.6%
632958
14.6%
331640
14.0%
1317858
7.9%
915690
 
7.0%
813193
 
5.9%
510046
 
4.5%
18922
 
4.0%
148169
 
3.6%
76722
 
3.0%
Other values (12)36061
16.0%
ValueCountFrequency (%)
18922
 
4.0%
24049
 
1.8%
331640
14.0%
444234
19.6%
510046
 
4.5%
632958
14.6%
76722
 
3.0%
813193
 
5.9%
915690
 
7.0%
103465
 
1.5%
ValueCountFrequency (%)
2275
 
< 0.1%
21152
 
0.1%
20182
 
0.1%
19952
 
0.4%
185406
 
2.4%
173352
 
1.5%
162667
 
1.2%
155032
 
2.2%
148169
3.6%
1317858
7.9%

Employee_code_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3269
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1550.665453
Minimum1
Maximum3795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:38.399152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile149
Q1713
median1452
Q32365
95-th percentile3187
Maximum3795
Range3794
Interquartile range (IQR)1652

Descriptive statistics

Standard deviation975.664631
Coefficient of variation (CV)0.6291909252
Kurtosis-1.054844758
Mean1550.665453
Median Absolute Deviation (MAD)813
Skewness0.2423795862
Sum349664205
Variance951921.4722
MonotonicityNot monotonic
2022-11-01T17:54:38.631083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2546628
 
0.3%
620502
 
0.2%
255492
 
0.2%
130408
 
0.2%
2153401
 
0.2%
1466355
 
0.2%
1494352
 
0.2%
64349
 
0.2%
751343
 
0.2%
184340
 
0.2%
Other values (3259)221323
98.2%
ValueCountFrequency (%)
180
< 0.1%
3132
0.1%
467
< 0.1%
588
< 0.1%
7144
0.1%
956
 
< 0.1%
1044
 
< 0.1%
1185
< 0.1%
12119
0.1%
1583
< 0.1%
ValueCountFrequency (%)
37951
 
< 0.1%
37941
 
< 0.1%
37931
 
< 0.1%
37921
 
< 0.1%
37913
< 0.1%
37901
 
< 0.1%
37892
< 0.1%
37881
 
< 0.1%
37872
< 0.1%
37863
< 0.1%

Aadhar_flag
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size220.3 KiB
True
188900 
False
36593 
ValueCountFrequency (%)
True188900
83.8%
False36593
 
16.2%
2022-11-01T17:54:38.847018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

PAN_flag
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size220.3 KiB
False
208043 
True
 
17450
ValueCountFrequency (%)
False208043
92.3%
True17450
 
7.7%
2022-11-01T17:54:38.998975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

VoterID_flag
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size220.3 KiB
False
192317 
True
33176 
ValueCountFrequency (%)
False192317
85.3%
True33176
 
14.7%
2022-11-01T17:54:39.182917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size220.3 KiB
False
220152 
True
 
5341
ValueCountFrequency (%)
False220152
97.6%
True5341
 
2.4%
2022-11-01T17:54:39.410851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size220.3 KiB
False
225011 
True
 
482
ValueCountFrequency (%)
False225011
99.8%
True482
 
0.2%
2022-11-01T17:54:39.582797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

PERFORM_CNS.SCORE
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct573
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.0404491
Minimum0
Maximum890
Zeros111773
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:39.839690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15
Q3680
95-th percentile825
Maximum890
Range890
Interquartile range (IQR)680

Descriptive statistics

Standard deviation338.8747837
Coefficient of variation (CV)1.156409583
Kurtosis-1.65222874
Mean293.0404491
Median Absolute Deviation (MAD)15
Skewness0.4244506554
Sum66078570
Variance114836.119
MonotonicityNot monotonic
2022-11-01T17:54:40.115610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0111773
49.6%
3008632
 
3.8%
7388473
 
3.8%
8257196
 
3.2%
153671
 
1.6%
173557
 
1.6%
7632972
 
1.3%
162815
 
1.2%
7082062
 
0.9%
7371943
 
0.9%
Other values (563)72399
32.1%
ValueCountFrequency (%)
0111773
49.6%
113
 
< 0.1%
14957
 
0.4%
153671
 
1.6%
162815
 
1.2%
173557
 
1.6%
181477
 
0.7%
3008632
 
3.8%
3019
 
< 0.1%
30217
 
< 0.1%
ValueCountFrequency (%)
8904
 
< 0.1%
8841
 
< 0.1%
87959
< 0.1%
8787
 
< 0.1%
8739
 
< 0.1%
87028
< 0.1%
8697
 
< 0.1%
8682
 
< 0.1%
8671
 
< 0.1%
8648
 
< 0.1%

PERFORM_CNS.SCORE.DESCRIPTION
Categorical

HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
No Bureau History Available
111773 
C-Very Low Risk
15715 
A-Very Low Risk
13790 
D-Very Low Risk
 
11134
B-Very Low Risk
 
9032
Other values (15)
64049 

Length

Max length55
Median length53
Mean length22.13364938
Min length10

Characters and Unicode

Total characters4990983
Distinct characters50
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Bureau History Available
2nd rowI-Medium Risk
3rd rowNo Bureau History Available
4th rowL-Very High Risk
5th rowNo Bureau History Available

Common Values

ValueCountFrequency (%)
No Bureau History Available111773
49.6%
C-Very Low Risk15715
 
7.0%
A-Very Low Risk13790
 
6.1%
D-Very Low Risk11134
 
4.9%
B-Very Low Risk9032
 
4.0%
M-Very High Risk8632
 
3.8%
F-Low Risk8309
 
3.7%
K-High Risk8107
 
3.6%
H-Medium Risk6695
 
3.0%
E-Low Risk5695
 
2.5%
Other values (10)26611
 
11.8%

Length

2022-11-01T17:54:40.391523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
available120478
14.9%
no116065
14.4%
history115444
14.3%
bureau111773
13.9%
risk101240
12.6%
low49671
 
6.2%
not19708
 
2.4%
c-very15715
 
1.9%
a-very13790
 
1.7%
scored12480
 
1.5%
Other values (35)130109
16.1%

Most occurring characters

ValueCountFrequency (%)
580980
 
11.6%
i388092
 
7.8%
a366409
 
7.3%
o353575
 
7.1%
e342634
 
6.9%
r307411
 
6.2%
u250244
 
5.0%
l243390
 
4.9%
s230305
 
4.6%
y178641
 
3.6%
Other values (40)1749302
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3413822
68.4%
Uppercase Letter873871
 
17.5%
Space Separator580980
 
11.6%
Dash Punctuation101240
 
2.0%
Other Punctuation12480
 
0.3%
Decimal Number2960
 
0.1%
Open Punctuation2815
 
0.1%
Close Punctuation2815
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i388092
11.4%
a366409
10.7%
o353575
10.4%
e342634
10.0%
r307411
9.0%
u250244
7.3%
l243390
7.1%
s230305
 
6.7%
y178641
 
5.2%
t165409
 
4.8%
Other values (12)587712
17.2%
Uppercase Letter
ValueCountFrequency (%)
H143667
16.4%
N135773
15.5%
A132052
15.1%
B120805
13.8%
R101240
11.6%
L68699
7.9%
V59425
6.8%
M20770
 
2.4%
S16151
 
1.8%
C15715
 
1.8%
Other values (9)59574
6.8%
Decimal Number
ValueCountFrequency (%)
31477
49.9%
61477
49.9%
53
 
0.1%
03
 
0.1%
Space Separator
ValueCountFrequency (%)
580980
100.0%
Dash Punctuation
ValueCountFrequency (%)
-101240
100.0%
Other Punctuation
ValueCountFrequency (%)
:12480
100.0%
Open Punctuation
ValueCountFrequency (%)
(2815
100.0%
Close Punctuation
ValueCountFrequency (%)
)2815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4287693
85.9%
Common703290
 
14.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i388092
 
9.1%
a366409
 
8.5%
o353575
 
8.2%
e342634
 
8.0%
r307411
 
7.2%
u250244
 
5.8%
l243390
 
5.7%
s230305
 
5.4%
y178641
 
4.2%
t165409
 
3.9%
Other values (31)1461583
34.1%
Common
ValueCountFrequency (%)
580980
82.6%
-101240
 
14.4%
:12480
 
1.8%
(2815
 
0.4%
)2815
 
0.4%
31477
 
0.2%
61477
 
0.2%
53
 
< 0.1%
03
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4990983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
580980
 
11.6%
i388092
 
7.8%
a366409
 
7.3%
o353575
 
7.1%
e342634
 
6.9%
r307411
 
6.2%
u250244
 
5.0%
l243390
 
4.9%
s230305
 
4.6%
y178641
 
3.6%
Other values (40)1749302
35.0%

PRI.NO.OF.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct107
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.462360251
Minimum0
Maximum453
Zeros111773
Zeros (%)49.6%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:40.637099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile11
Maximum453
Range453
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.223011518
Coefficient of variation (CV)2.121140283
Kurtosis426.083019
Mean2.462360251
Median Absolute Deviation (MAD)1
Skewness9.857231997
Sum555245
Variance27.27984932
MonotonicityNot monotonic
2022-11-01T17:54:40.918644image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0111773
49.6%
134154
 
15.1%
219426
 
8.6%
312787
 
5.7%
49159
 
4.1%
57079
 
3.1%
65462
 
2.4%
74332
 
1.9%
83488
 
1.5%
92815
 
1.2%
Other values (97)15018
 
6.7%
ValueCountFrequency (%)
0111773
49.6%
134154
 
15.1%
219426
 
8.6%
312787
 
5.7%
49159
 
4.1%
57079
 
3.1%
65462
 
2.4%
74332
 
1.9%
83488
 
1.5%
92815
 
1.2%
ValueCountFrequency (%)
4531
< 0.1%
3541
< 0.1%
2711
< 0.1%
1941
< 0.1%
1482
< 0.1%
1471
< 0.1%
1361
< 0.1%
1321
< 0.1%
1311
< 0.1%
1241
< 0.1%

PRI.ACTIVE.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.053766636
Minimum0
Maximum144
Zeros131395
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:41.178210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile5
Maximum144
Range144
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.95201484
Coefficient of variation (CV)1.852416629
Kurtosis156.3509839
Mean1.053766636
Median Absolute Deviation (MAD)0
Skewness5.376997268
Sum237617
Variance3.810361934
MonotonicityNot monotonic
2022-11-01T17:54:41.490116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0131395
58.3%
141050
 
18.2%
221138
 
9.4%
312044
 
5.3%
47306
 
3.2%
54447
 
2.0%
62741
 
1.2%
71766
 
0.8%
81171
 
0.5%
9740
 
0.3%
Other values (30)1695
 
0.8%
ValueCountFrequency (%)
0131395
58.3%
141050
 
18.2%
221138
 
9.4%
312044
 
5.3%
47306
 
3.2%
54447
 
2.0%
62741
 
1.2%
71766
 
0.8%
81171
 
0.5%
9740
 
0.3%
ValueCountFrequency (%)
1441
< 0.1%
651
< 0.1%
521
< 0.1%
431
< 0.1%
421
< 0.1%
391
< 0.1%
372
< 0.1%
352
< 0.1%
342
< 0.1%
322
< 0.1%

PRI.OVERDUE.ACCTS
Real number (ℝ≥0)

ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1589894143
Minimum0
Maximum25
Zeros199703
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:41.706049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5534152006
Coefficient of variation (CV)3.480830488
Kurtosis124.8961637
Mean0.1589894143
Median Absolute Deviation (MAD)0
Skewness7.486695923
Sum35851
Variance0.3062683843
MonotonicityNot monotonic
2022-11-01T17:54:41.917987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0199703
88.6%
119596
 
8.7%
24226
 
1.9%
31175
 
0.5%
4399
 
0.2%
5165
 
0.1%
696
 
< 0.1%
738
 
< 0.1%
826
 
< 0.1%
924
 
< 0.1%
Other values (12)45
 
< 0.1%
ValueCountFrequency (%)
0199703
88.6%
119596
 
8.7%
24226
 
1.9%
31175
 
0.5%
4399
 
0.2%
5165
 
0.1%
696
 
< 0.1%
738
 
< 0.1%
826
 
< 0.1%
924
 
< 0.1%
ValueCountFrequency (%)
251
 
< 0.1%
231
 
< 0.1%
191
 
< 0.1%
182
 
< 0.1%
172
 
< 0.1%
161
 
< 0.1%
151
 
< 0.1%
145
< 0.1%
135
< 0.1%
128
< 0.1%

PRI.CURRENT.BALANCE
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct70044
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean168481.3163
Minimum-6678296
Maximum96524920
Zeros136011
Zeros (%)60.3%
Negative436
Negative (%)0.2%
Memory size1.7 MiB
2022-11-01T17:54:42.134634image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-6678296
5-th percentile0
Q10
median0
Q336300
95-th percentile817893.4
Maximum96524920
Range103203216
Interquartile range (IQR)36300

Descriptive statistics

Standard deviation951669.1721
Coefficient of variation (CV)5.648514584
Kurtosis1597.186355
Mean168481.3163
Median Absolute Deviation (MAD)0
Skewness29.25624693
Sum3.799135745 × 1010
Variance9.056742132 × 1011
MonotonicityNot monotonic
2022-11-01T17:54:42.382560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0136011
60.3%
800120
 
0.1%
400119
 
0.1%
3000099
 
< 0.1%
10000080
 
< 0.1%
5000080
 
< 0.1%
4000072
 
< 0.1%
2500072
 
< 0.1%
2000062
 
< 0.1%
6000061
 
< 0.1%
Other values (70034)88717
39.3%
ValueCountFrequency (%)
-66782961
< 0.1%
-20183091
< 0.1%
-17384151
< 0.1%
-14083141
< 0.1%
-13064491
< 0.1%
-11782421
< 0.1%
-11081141
< 0.1%
-9316441
< 0.1%
-7635991
< 0.1%
-7540601
< 0.1%
ValueCountFrequency (%)
965249201
< 0.1%
756034001
< 0.1%
664061601
< 0.1%
635313201
< 0.1%
633590401
< 0.1%
613676881
< 0.1%
563858241
< 0.1%
561635441
< 0.1%
525031521
< 0.1%
523679601
< 0.1%

PRI.SANCTIONED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct43743
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222073.6394
Minimum0
Maximum1000000000
Zeros132449
Zeros (%)58.7%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:42.626484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q364900
95-th percentile1046668.2
Maximum1000000000
Range1000000000
Interquartile range (IQR)64900

Descriptive statistics

Standard deviation2411721.515
Coefficient of variation (CV)10.86000806
Kurtosis131068.3347
Mean222073.6394
Median Absolute Deviation (MAD)0
Skewness319.533663
Sum5.007605118 × 1010
Variance5.816400667 × 1012
MonotonicityNot monotonic
2022-11-01T17:54:42.862439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0132449
58.7%
500001456
 
0.6%
300001406
 
0.6%
100000942
 
0.4%
25000935
 
0.4%
40000843
 
0.4%
20000824
 
0.4%
200000593
 
0.3%
60000586
 
0.3%
15000553
 
0.2%
Other values (43733)84906
37.7%
ValueCountFrequency (%)
0132449
58.7%
135
 
< 0.1%
224
 
< 0.1%
320
 
< 0.1%
420
 
< 0.1%
515
 
< 0.1%
69
 
< 0.1%
713
 
< 0.1%
815
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
10000000001
< 0.1%
1058657121
< 0.1%
1004250001
< 0.1%
926228161
< 0.1%
863238881
< 0.1%
803275601
< 0.1%
790127521
< 0.1%
761287121
< 0.1%
698474561
< 0.1%
698280001
< 0.1%

PRI.DISBURSED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct47206
Distinct (%)20.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean221609.8144
Minimum0
Maximum1000000000
Zeros132559
Zeros (%)58.8%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:45.136969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q362990
95-th percentile1042532
Maximum1000000000
Range1000000000
Interquartile range (IQR)62990

Descriptive statistics

Standard deviation2414697.439
Coefficient of variation (CV)10.89616652
Kurtosis130424.4242
Mean221609.8144
Median Absolute Deviation (MAD)0
Skewness318.4004683
Sum4.997146188 × 1010
Variance5.830763724 × 1012
MonotonicityNot monotonic
2022-11-01T17:54:45.378006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0132559
58.8%
500001354
 
0.6%
300001302
 
0.6%
100000917
 
0.4%
40000764
 
0.3%
25000739
 
0.3%
20000638
 
0.3%
200000600
 
0.3%
300000539
 
0.2%
60000518
 
0.2%
Other values (47196)85563
37.9%
ValueCountFrequency (%)
0132559
58.8%
144
 
< 0.1%
225
 
< 0.1%
320
 
< 0.1%
419
 
< 0.1%
515
 
< 0.1%
69
 
< 0.1%
713
 
< 0.1%
815
 
< 0.1%
917
 
< 0.1%
ValueCountFrequency (%)
10000000001
< 0.1%
1057557121
< 0.1%
1004250001
< 0.1%
926287281
< 0.1%
860247841
< 0.1%
803491681
< 0.1%
790127521
< 0.1%
761287121
< 0.1%
698474561
< 0.1%
697159441
< 0.1%

SEC.NO.OF.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06012160023
Minimum0
Maximum52
Zeros219731
Zeros (%)97.4%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:45.589246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum52
Range52
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6331042657
Coefficient of variation (CV)10.53039612
Kurtosis1268.943982
Mean0.06012160023
Median Absolute Deviation (MAD)0
Skewness27.84235193
Sum13557
Variance0.4008210112
MonotonicityNot monotonic
2022-11-01T17:54:45.794953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0219731
97.4%
13396
 
1.5%
21022
 
0.5%
3438
 
0.2%
4289
 
0.1%
5147
 
0.1%
6115
 
0.1%
775
 
< 0.1%
867
 
< 0.1%
937
 
< 0.1%
Other values (27)176
 
0.1%
ValueCountFrequency (%)
0219731
97.4%
13396
 
1.5%
21022
 
0.5%
3438
 
0.2%
4289
 
0.1%
5147
 
0.1%
6115
 
0.1%
775
 
< 0.1%
867
 
< 0.1%
937
 
< 0.1%
ValueCountFrequency (%)
521
 
< 0.1%
462
< 0.1%
421
 
< 0.1%
382
< 0.1%
371
 
< 0.1%
351
 
< 0.1%
342
< 0.1%
314
< 0.1%
302
< 0.1%
291
 
< 0.1%

SEC.ACTIVE.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02821373612
Minimum0
Maximum36
Zeros221737
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:45.995489image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3189458665
Coefficient of variation (CV)11.30463066
Kurtosis1753.782524
Mean0.02821373612
Median Absolute Deviation (MAD)0
Skewness30.4096604
Sum6362
Variance0.1017264657
MonotonicityNot monotonic
2022-11-01T17:54:46.164758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0221737
98.3%
12637
 
1.2%
2627
 
0.3%
3193
 
0.1%
4116
 
0.1%
564
 
< 0.1%
632
 
< 0.1%
722
 
< 0.1%
817
 
< 0.1%
910
 
< 0.1%
Other values (13)38
 
< 0.1%
ValueCountFrequency (%)
0221737
98.3%
12637
 
1.2%
2627
 
0.3%
3193
 
0.1%
4116
 
0.1%
564
 
< 0.1%
632
 
< 0.1%
722
 
< 0.1%
817
 
< 0.1%
910
 
< 0.1%
ValueCountFrequency (%)
361
 
< 0.1%
261
 
< 0.1%
222
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
171
 
< 0.1%
162
< 0.1%
154
< 0.1%
141
 
< 0.1%
133
< 0.1%

SEC.OVERDUE.ACCTS
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00736164759
Minimum0
Maximum8
Zeros224183
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:46.349663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1123006844
Coefficient of variation (CV)15.25483025
Kurtosis855.9649875
Mean0.00736164759
Median Absolute Deviation (MAD)0
Skewness24.01431522
Sum1660
Variance0.01261144371
MonotonicityNot monotonic
2022-11-01T17:54:46.496813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0224183
99.4%
11104
 
0.5%
2124
 
0.1%
347
 
< 0.1%
419
 
< 0.1%
58
 
< 0.1%
66
 
< 0.1%
81
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
0224183
99.4%
11104
 
0.5%
2124
 
0.1%
347
 
< 0.1%
419
 
< 0.1%
58
 
< 0.1%
66
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
71
 
< 0.1%
66
 
< 0.1%
58
 
< 0.1%
419
 
< 0.1%
347
 
< 0.1%
2124
 
0.1%
11104
 
0.5%
0224183
99.4%

SEC.CURRENT.BALANCE
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct3197
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5569.681853
Minimum-574647
Maximum36032852
Zeros222182
Zeros (%)98.5%
Negative60
Negative (%)< 0.1%
Memory size1.7 MiB
2022-11-01T17:54:46.712972image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-574647
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum36032852
Range36607499
Interquartile range (IQR)0

Descriptive statistics

Standard deviation172928.1293
Coefficient of variation (CV)31.04811619
Kurtosis16744.54951
Mean5569.681853
Median Absolute Deviation (MAD)0
Skewness107.0091863
Sum1255924270
Variance2.990413791 × 1010
MonotonicityNot monotonic
2022-11-01T17:54:46.951262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0222182
98.5%
80010
 
< 0.1%
1008
 
< 0.1%
4008
 
< 0.1%
12006
 
< 0.1%
5895
 
< 0.1%
-15
 
< 0.1%
16004
 
< 0.1%
10704
 
< 0.1%
14
 
< 0.1%
Other values (3187)3257
 
1.4%
ValueCountFrequency (%)
-5746471
< 0.1%
-2397821
< 0.1%
-1555271
< 0.1%
-1171381
< 0.1%
-312901
< 0.1%
-200001
< 0.1%
-96251
< 0.1%
-86061
< 0.1%
-77301
< 0.1%
-73701
< 0.1%
ValueCountFrequency (%)
360328521
< 0.1%
295605401
< 0.1%
246920241
< 0.1%
224971721
< 0.1%
196382801
< 0.1%
136078821
< 0.1%
120801021
< 0.1%
107792611
< 0.1%
107160391
< 0.1%
98011341
< 0.1%

SEC.SANCTIONED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2195
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7489.1866
Minimum0
Maximum30000000
Zeros221816
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:47.183043image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30000000
Range30000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation186043.2484
Coefficient of variation (CV)24.84158272
Kurtosis8423.825453
Mean7489.1866
Median Absolute Deviation (MAD)0
Skewness74.21689332
Sum1688759154
Variance3.461209027 × 1010
MonotonicityNot monotonic
2022-11-01T17:54:47.414817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0221816
98.4%
5000082
 
< 0.1%
10000060
 
< 0.1%
3000043
 
< 0.1%
20000038
 
< 0.1%
4000037
 
< 0.1%
1500036
 
< 0.1%
2500034
 
< 0.1%
1000032
 
< 0.1%
30000030
 
< 0.1%
Other values (2185)3285
 
1.5%
ValueCountFrequency (%)
0221816
98.4%
16
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
191
 
< 0.1%
231
 
< 0.1%
301
 
< 0.1%
321
 
< 0.1%
521
 
< 0.1%
541
 
< 0.1%
ValueCountFrequency (%)
300000001
< 0.1%
268882001
< 0.1%
250000001
< 0.1%
198000001
< 0.1%
186910021
< 0.1%
136078821
< 0.1%
126260001
< 0.1%
125119901
< 0.1%
120143001
< 0.1%
119000001
< 0.1%

SEC.DISBURSED.AMOUNT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct2519
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7371.103569
Minimum0
Maximum30000000
Zeros221846
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:47.655402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30000000
Range30000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185470.3088
Coefficient of variation (CV)25.16181017
Kurtosis8521.088802
Mean7371.103569
Median Absolute Deviation (MAD)0
Skewness74.71985798
Sum1662132257
Variance3.439923543 × 1010
MonotonicityNot monotonic
2022-11-01T17:54:47.875363image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0221846
98.4%
5000058
 
< 0.1%
10000046
 
< 0.1%
20000036
 
< 0.1%
30000029
 
< 0.1%
4000029
 
< 0.1%
3000026
 
< 0.1%
50000025
 
< 0.1%
15000023
 
< 0.1%
40000021
 
< 0.1%
Other values (2509)3354
 
1.5%
ValueCountFrequency (%)
0221846
98.4%
15
 
< 0.1%
82
 
< 0.1%
91
 
< 0.1%
191
 
< 0.1%
231
 
< 0.1%
301
 
< 0.1%
321
 
< 0.1%
521
 
< 0.1%
541
 
< 0.1%
ValueCountFrequency (%)
300000001
< 0.1%
268882001
< 0.1%
250000001
< 0.1%
198000001
< 0.1%
186910021
< 0.1%
136078821
< 0.1%
126260001
< 0.1%
125119901
< 0.1%
120143001
< 0.1%
119000001
< 0.1%

PRIMARY.INSTAL.AMT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct27608
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12992.45694
Minimum0
Maximum25642806
Zeros153544
Zeros (%)68.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:48.117238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32045
95-th percentile26361.4
Maximum25642806
Range25642806
Interquartile range (IQR)2045

Descriptive statistics

Standard deviation149708.4301
Coefficient of variation (CV)11.52271897
Kurtosis8574.41736
Mean12992.45694
Median Absolute Deviation (MAD)0
Skewness71.5253121
Sum2929708092
Variance2.241261403 × 1010
MonotonicityNot monotonic
2022-11-01T17:54:48.356282image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0153544
68.1%
1620287
 
0.1%
1500152
 
0.1%
2000140
 
0.1%
1600139
 
0.1%
2500133
 
0.1%
1149128
 
0.1%
1250121
 
0.1%
1700109
 
< 0.1%
1350100
 
< 0.1%
Other values (27598)70640
31.3%
ValueCountFrequency (%)
0153544
68.1%
15
 
< 0.1%
24
 
< 0.1%
319
 
< 0.1%
415
 
< 0.1%
512
 
< 0.1%
622
 
< 0.1%
713
 
< 0.1%
813
 
< 0.1%
918
 
< 0.1%
ValueCountFrequency (%)
256428061
< 0.1%
207665531
< 0.1%
174088221
< 0.1%
155185461
< 0.1%
154204111
< 0.1%
150199141
< 0.1%
145992521
< 0.1%
113055791
< 0.1%
84700591
< 0.1%
76631101
< 0.1%

SEC.INSTAL.AMT
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct1890
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.684478
Minimum0
Maximum4170901
Zeros223313
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:48.589270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4170901
Range4170901
Interquartile range (IQR)0

Descriptive statistics

Standard deviation15756.16957
Coefficient of variation (CV)48.37863217
Kurtosis32466.62925
Mean325.684478
Median Absolute Deviation (MAD)0
Skewness152.8457066
Sum73439570
Variance248256879.4
MonotonicityNot monotonic
2022-11-01T17:54:48.826145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0223313
99.0%
21007
 
< 0.1%
12326
 
< 0.1%
10656
 
< 0.1%
11006
 
< 0.1%
50006
 
< 0.1%
11675
 
< 0.1%
8335
 
< 0.1%
15655
 
< 0.1%
18345
 
< 0.1%
Other values (1880)2129
 
0.9%
ValueCountFrequency (%)
0223313
99.0%
13
 
< 0.1%
21
 
< 0.1%
31
 
< 0.1%
51
 
< 0.1%
61
 
< 0.1%
91
 
< 0.1%
111
 
< 0.1%
122
 
< 0.1%
162
 
< 0.1%
ValueCountFrequency (%)
41709011
< 0.1%
32467101
< 0.1%
18140001
< 0.1%
16612201
< 0.1%
15899461
< 0.1%
14476001
< 0.1%
12311661
< 0.1%
11131181
< 0.1%
10200001
< 0.1%
8424831
< 0.1%

NEW.ACCTS.IN.LAST.SIX.MONTHS
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3866018014
Minimum0
Maximum35
Zeros174944
Zeros (%)77.6%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:49.026680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum35
Range35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.9596677387
Coefficient of variation (CV)2.482315745
Kurtosis46.36151075
Mean0.3866018014
Median Absolute Deviation (MAD)0
Skewness4.786404025
Sum87176
Variance0.9209621688
MonotonicityNot monotonic
2022-11-01T17:54:49.211588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0174944
77.6%
131361
 
13.9%
210806
 
4.8%
34375
 
1.9%
41918
 
0.9%
5947
 
0.4%
6473
 
0.2%
7293
 
0.1%
8143
 
0.1%
978
 
< 0.1%
Other values (16)155
 
0.1%
ValueCountFrequency (%)
0174944
77.6%
131361
 
13.9%
210806
 
4.8%
34375
 
1.9%
41918
 
0.9%
5947
 
0.4%
6473
 
0.2%
7293
 
0.1%
8143
 
0.1%
978
 
< 0.1%
ValueCountFrequency (%)
351
 
< 0.1%
281
 
< 0.1%
232
 
< 0.1%
221
 
< 0.1%
211
 
< 0.1%
202
 
< 0.1%
192
 
< 0.1%
182
 
< 0.1%
175
< 0.1%
166
< 0.1%

DELINQUENT.ACCTS.IN.LAST.SIX.MONTHS
Real number (ℝ≥0)

ZEROS

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09870816389
Minimum0
Maximum20
Zeros207647
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:49.412126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3863763545
Coefficient of variation (CV)3.914330277
Kurtosis98.8217849
Mean0.09870816389
Median Absolute Deviation (MAD)0
Skewness6.620306255
Sum22258
Variance0.1492866873
MonotonicityNot monotonic
2022-11-01T17:54:49.581401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0207647
92.1%
114680
 
6.5%
22405
 
1.1%
3519
 
0.2%
4136
 
0.1%
556
 
< 0.1%
620
 
< 0.1%
712
 
< 0.1%
87
 
< 0.1%
123
 
< 0.1%
Other values (4)8
 
< 0.1%
ValueCountFrequency (%)
0207647
92.1%
114680
 
6.5%
22405
 
1.1%
3519
 
0.2%
4136
 
0.1%
556
 
< 0.1%
620
 
< 0.1%
712
 
< 0.1%
87
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
201
 
< 0.1%
123
 
< 0.1%
113
 
< 0.1%
102
 
< 0.1%
92
 
< 0.1%
87
 
< 0.1%
712
 
< 0.1%
620
 
< 0.1%
556
< 0.1%
4136
0.1%

AVERAGE.ACCT.AGE
Categorical

HIGH CARDINALITY

Distinct192
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0yrs 0mon
114135 
0yrs 6mon
 
5907
0yrs 7mon
 
5254
0yrs 11mon
 
5110
0yrs 10mon
 
5005
Other values (187)
90082 

Length

Max length11
Median length9
Mean length9.07802016
Min length9

Characters and Unicode

Total characters2047030
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)< 0.1%

Sample

1st row0yrs 0mon
2nd row1yrs 11mon
3rd row0yrs 0mon
4th row0yrs 8mon
5th row0yrs 0mon

Common Values

ValueCountFrequency (%)
0yrs 0mon114135
50.6%
0yrs 6mon5907
 
2.6%
0yrs 7mon5254
 
2.3%
0yrs 11mon5110
 
2.3%
0yrs 10mon5005
 
2.2%
0yrs 9mon4895
 
2.2%
1yrs 0mon4890
 
2.2%
0yrs 8mon4785
 
2.1%
1yrs 1mon4363
 
1.9%
0yrs 5mon4271
 
1.9%
Other values (182)66878
29.7%

Length

2022-11-01T17:54:49.808000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0yrs162056
35.9%
0mon122525
27.2%
1yrs35860
 
8.0%
2yrs14563
 
3.2%
6mon10866
 
2.4%
1mon9887
 
2.2%
7mon9685
 
2.1%
4mon9571
 
2.1%
3mon9508
 
2.1%
2mon9464
 
2.1%
Other values (24)57001
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0293340
14.3%
r225493
11.0%
s225493
11.0%
225493
11.0%
m225493
11.0%
o225493
11.0%
n225493
11.0%
y225493
11.0%
172046
 
3.5%
224086
 
1.2%
Other values (7)79107
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1352958
66.1%
Decimal Number468579
 
22.9%
Space Separator225493
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0293340
62.6%
172046
 
15.4%
224086
 
5.1%
316041
 
3.4%
412576
 
2.7%
611659
 
2.5%
510911
 
2.3%
710142
 
2.2%
88993
 
1.9%
98785
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
r225493
16.7%
s225493
16.7%
m225493
16.7%
o225493
16.7%
n225493
16.7%
y225493
16.7%
Space Separator
ValueCountFrequency (%)
225493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1352958
66.1%
Common694072
33.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0293340
42.3%
225493
32.5%
172046
 
10.4%
224086
 
3.5%
316041
 
2.3%
412576
 
1.8%
611659
 
1.7%
510911
 
1.6%
710142
 
1.5%
88993
 
1.3%
Latin
ValueCountFrequency (%)
r225493
16.7%
s225493
16.7%
m225493
16.7%
o225493
16.7%
n225493
16.7%
y225493
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2047030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0293340
14.3%
r225493
11.0%
s225493
11.0%
225493
11.0%
m225493
11.0%
o225493
11.0%
n225493
11.0%
y225493
11.0%
172046
 
3.5%
224086
 
1.2%
Other values (7)79107
 
3.9%

CREDIT.HISTORY.LENGTH
Categorical

HIGH CARDINALITY

Distinct291
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0yrs 0mon
113894 
0yrs 6mon
 
4670
2yrs 1mon
 
4596
0yrs 7mon
 
3952
2yrs 0mon
 
3711
Other values (286)
94670 

Length

Max length11
Median length9
Mean length9.092490676
Min length9

Characters and Unicode

Total characters2050293
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)< 0.1%

Sample

1st row0yrs 0mon
2nd row1yrs 11mon
3rd row0yrs 0mon
4th row1yrs 3mon
5th row0yrs 0mon

Common Values

ValueCountFrequency (%)
0yrs 0mon113894
50.5%
0yrs 6mon4670
 
2.1%
2yrs 1mon4596
 
2.0%
0yrs 7mon3952
 
1.8%
2yrs 0mon3711
 
1.6%
1yrs 0mon3290
 
1.5%
1yrs 1mon2960
 
1.3%
0yrs 11mon2564
 
1.1%
0yrs 8mon2401
 
1.1%
0yrs 9mon2351
 
1.0%
Other values (281)81104
36.0%

Length

2022-11-01T17:54:50.024154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0yrs141964
31.5%
0mon125313
27.8%
1yrs26011
 
5.8%
2yrs22097
 
4.9%
1mon13351
 
3.0%
3yrs11669
 
2.6%
6mon11021
 
2.4%
7mon10010
 
2.2%
2mon9140
 
2.0%
11mon8800
 
2.0%
Other values (37)71610
15.9%

Most occurring characters

ValueCountFrequency (%)
0276257
13.5%
r225493
11.0%
s225493
11.0%
225493
11.0%
m225493
11.0%
o225493
11.0%
n225493
11.0%
y225493
11.0%
170096
 
3.4%
232046
 
1.6%
Other values (7)93443
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1352958
66.0%
Decimal Number471842
 
23.0%
Space Separator225493
 
11.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0276257
58.5%
170096
 
14.9%
232046
 
6.8%
320739
 
4.4%
415656
 
3.3%
613961
 
3.0%
512996
 
2.8%
711955
 
2.5%
89215
 
2.0%
98921
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
r225493
16.7%
s225493
16.7%
m225493
16.7%
o225493
16.7%
n225493
16.7%
y225493
16.7%
Space Separator
ValueCountFrequency (%)
225493
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1352958
66.0%
Common697335
34.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0276257
39.6%
225493
32.3%
170096
 
10.1%
232046
 
4.6%
320739
 
3.0%
415656
 
2.2%
613961
 
2.0%
512996
 
1.9%
711955
 
1.7%
89215
 
1.3%
Latin
ValueCountFrequency (%)
r225493
16.7%
s225493
16.7%
m225493
16.7%
o225493
16.7%
n225493
16.7%
y225493
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2050293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0276257
13.5%
r225493
11.0%
s225493
11.0%
225493
11.0%
m225493
11.0%
o225493
11.0%
n225493
11.0%
y225493
11.0%
170096
 
3.4%
232046
 
1.6%
Other values (7)93443
 
4.6%

NO.OF_INQUIRIES
Real number (ℝ≥0)

ZEROS

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2088446205
Minimum0
Maximum36
Zeros194990
Zeros (%)86.5%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2022-11-01T17:54:50.233280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7100854515
Coefficient of variation (CV)3.4000658
Kurtosis132.0497536
Mean0.2088446205
Median Absolute Deviation (MAD)0
Skewness7.862951512
Sum47093
Variance0.5042213484
MonotonicityNot monotonic
2022-11-01T17:54:50.411424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0194990
86.5%
121794
 
9.7%
25294
 
2.3%
31724
 
0.8%
4745
 
0.3%
5331
 
0.1%
6234
 
0.1%
7133
 
0.1%
8103
 
< 0.1%
941
 
< 0.1%
Other values (15)104
 
< 0.1%
ValueCountFrequency (%)
0194990
86.5%
121794
 
9.7%
25294
 
2.3%
31724
 
0.8%
4745
 
0.3%
5331
 
0.1%
6234
 
0.1%
7133
 
0.1%
8103
 
< 0.1%
941
 
< 0.1%
ValueCountFrequency (%)
361
 
< 0.1%
281
 
< 0.1%
231
 
< 0.1%
221
 
< 0.1%
201
 
< 0.1%
196
< 0.1%
184
< 0.1%
174
< 0.1%
163
< 0.1%
157
< 0.1%

loan_default
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0
176526 
1
48967 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters225493
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0176526
78.3%
148967
 
21.7%

Length

2022-11-01T17:54:50.595379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-01T17:54:50.755351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0176526
78.3%
148967
 
21.7%

Most occurring characters

ValueCountFrequency (%)
0176526
78.3%
148967
 
21.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number225493
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0176526
78.3%
148967
 
21.7%

Most occurring scripts

ValueCountFrequency (%)
Common225493
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0176526
78.3%
148967
 
21.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII225493
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0176526
78.3%
148967
 
21.7%

Interactions

2022-11-01T17:54:12.655552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:48:13.356678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:48:25.430912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:48:40.658480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:49:05.432009image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:49:17.885699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:49:30.331975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:49:43.131510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:49:59.623632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:50:16.816721image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:50:29.200063image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:50:38.857787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:50:57.540657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:51:13.272909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:51:26.537761image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:51:39.085873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:51:48.719434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:52:02.021330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:52:18.118032image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:52:32.416110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:52:42.621245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:52:53.414285image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:02.478782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:13.239250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:23.510312image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:32.790675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:42.501983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:52.599999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:54:02.396333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:54:12.994135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:48:13.771906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:48:25.970187image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:48:41.063491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:49:05.862875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:49:18.304631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-11-01T17:53:02.110719image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:12.834197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:23.162641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:32.459637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:42.163435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:53:52.214554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:54:02.076033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-11-01T17:54:12.304353image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-11-01T17:54:51.053907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-01T17:54:51.793685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-01T17:54:52.496083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-01T17:54:53.196970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-01T17:54:53.818672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-01T17:54:54.166697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-01T17:54:22.967622image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-01T17:54:27.211371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexUniqueIDdisbursed_amountasset_costltvbranch_idsupplier_idmanufacturer_idCurrent_pincode_IDDate.of.BirthEmployment.TypeDisbursalDateState_IDEmployee_code_IDAadhar_flagPAN_flagVoterID_flagDriving_flagPassport_flagPERFORM_CNS.SCOREPERFORM_CNS.SCORE.DESCRIPTIONPRI.NO.OF.ACCTSPRI.ACTIVE.ACCTSPRI.OVERDUE.ACCTSPRI.CURRENT.BALANCEPRI.SANCTIONED.AMOUNTPRI.DISBURSED.AMOUNTSEC.NO.OF.ACCTSSEC.ACTIVE.ACCTSSEC.OVERDUE.ACCTSSEC.CURRENT.BALANCESEC.SANCTIONED.AMOUNTSEC.DISBURSED.AMOUNTPRIMARY.INSTAL.AMTSEC.INSTAL.AMTNEW.ACCTS.IN.LAST.SIX.MONTHSDELINQUENT.ACCTS.IN.LAST.SIX.MONTHSAVERAGE.ACCT.AGECREDIT.HISTORY.LENGTHNO.OF_INQUIRIESloan_default
00420825505785840089.5567228074514411984-01-01Salaried2018-03-0861998TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
11537409471456555073.2367228074515021985-07-31Self employed2018-09-2661998TrueFalseFalseFalseFalse598I-Medium Risk11127600502005020000000019910011yrs 11mon1yrs 11mon01
22417566532786136089.6367228074514971985-08-24Self employed2018-01-0861998TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
33624493575136611388.4867228074515011993-12-30Self employed2018-10-2661998TrueFalseFalseFalseFalse305L-Very High Risk300000000000310000yrs 8mon1yrs 3mon11
44539055523786030088.3967228074514951977-09-12Self employed2018-09-2661998TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon11
55518279545136190089.6667228074515011990-08-09Self employed2018-09-1961998TrueFalseFalseFalseFalse825A-Very Low Risk20000000000013470001yrs 9mon2yrs 0mon00
66529269463496150076.4267228074515021988-01-06Salaried2018-09-2361998TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
77510278438946190071.8967228074515011989-04-10Salaried2018-09-1661998TrueFalseFalseFalseFalse17Not Scored: Not Enough Info available on the customer11072879745007450000000000000yrs 2mon0yrs 2mon00
88490213537136197389.5667228074514971991-11-15Self employed2018-05-0961998TrueFalseFalseFalseFalse718D-Very Low Risk110-4136538436538400000000004yrs 8mon4yrs 8mon10
99510980526036130086.9567228074514922068-01-06Salaried2018-09-1661998FalseFalseTrueFalseFalse818A-Very Low Risk10000000000026080001yrs 7mon1yrs 7mon00

Last rows

df_indexUniqueIDdisbursed_amountasset_costltvbranch_idsupplier_idmanufacturer_idCurrent_pincode_IDDate.of.BirthEmployment.TypeDisbursalDateState_IDEmployee_code_IDAadhar_flagPAN_flagVoterID_flagDriving_flagPassport_flagPERFORM_CNS.SCOREPERFORM_CNS.SCORE.DESCRIPTIONPRI.NO.OF.ACCTSPRI.ACTIVE.ACCTSPRI.OVERDUE.ACCTSPRI.CURRENT.BALANCEPRI.SANCTIONED.AMOUNTPRI.DISBURSED.AMOUNTSEC.NO.OF.ACCTSSEC.ACTIVE.ACCTSSEC.OVERDUE.ACCTSSEC.CURRENT.BALANCESEC.SANCTIONED.AMOUNTSEC.DISBURSED.AMOUNTPRIMARY.INSTAL.AMTSEC.INSTAL.AMTNEW.ACCTS.IN.LAST.SIX.MONTHSDELINQUENT.ACCTS.IN.LAST.SIX.MONTHSAVERAGE.ACCT.AGECREDIT.HISTORY.LENGTHNO.OF_INQUIRIESloan_default
225483233144613161560596900183.0434230248610442063-06-15Salaried2018-10-2463705TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
225484233145606146498036697376.1534210814510511985-12-23Self employed2018-10-2363705TrueFalseFalseFalseFalse690E-Low Risk74013064856298022600000016720200yrs 9mon2yrs 6mon10
225485233146622612384395296574.5834207004810511982-07-23Self employed2018-10-2663705TrueFalseFalseFalseFalse738C-Very Low Risk2207001148391483900000000200yrs 3mon0yrs 3mon00
2254862331476456977262310540569.7334207004810511989-06-19Salaried2018-10-3163705TrueFalseFalseFalseFalse755C-Very Low Risk44020142227662423797700000000100yrs 9mon1yrs 0mon00
225487233148613494428946033472.9334207004810511993-08-07Salaried2018-10-2463705TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
2254882331496264326321310540560.7234207004810501988-01-08Salaried2018-10-2663705FalseFalseTrueFalseFalse735D-Very Low Risk43039044341613341613300000040840001yrs 9mon3yrs 3mon00
2254892331506061417365110060074.953423775519901988-05-12Self employed2018-10-2363705FalseFalseTrueFalseFalse825A-Very Low Risk10000000000015650000yrs 6mon0yrs 6mon00
225490233151613658334847121248.4577221868622991976-01-06Salaried2018-10-2443479TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
225491233152548084342597328649.1077221868622991994-03-26Salaried2018-09-2943479TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00
2254922331536302137575111600966.8177221868622991984-02-18Salaried2018-10-2743479TrueFalseFalseFalseFalse0No Bureau History Available00000000000000000yrs 0mon0yrs 0mon00